evaluation.py 文件源码

def log_norm(expr_list, name):
    """

    :param expr_list:
    :param name:
    :return:
    """
    n_elems = 0
    norm = 0.
    for e in nest.flatten(expr_list):
        n_elems += tf.reduce_prod(tf.shape(e))
        norm += tf.reduce_sum(e**2)
    norm /= tf.to_float(n_elems)
    tf.summary.scalar(name, norm)
    return norm